The “Système National des Données de Santé” (the French National Health Data System – SNDS) consists of individual data from three databases: the inter-scheme consumption datamart, i.e. the national claims database (DCIR), the national hospital discharge database (PMSI), and the national causes-of-death register. The SNDS covers continuously around 99% of the French population, i.e. more than 67 million people.

Mc Nemar test regarding changes in prevalence consumptions.

Common variables between FREGAT and SNDS databases used for linking.

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Dataset

This dataset was created by Feimu Zhao

Contents

Introduction: The objective of this study was to evaluate the complementarity of the French national health database (Système national des données de Santé, SNDS) and the Dijon Stroke Registry for the epidemiology of stroke patients with anticoagulated atrial fibrillation (AF). Methods: The SNDS collects healthcare prescriptions and procedures reimbursed by the French national health insurance for almost all of the 66 million individuals living in France. A previously published algorithm was used to identify AF newly treated with oral anticoagulants. The Dijon Stroke Registry is a population-based study covering the residents of the city of Dijon since 1985 and records all stroke cases of the area. We compared the proportions of stroke patients with anticoagulated AF in the city of Dijon identified in SNDS databases to those registered in the Dijon Stroke Registry. Results: For the period 2013–2017 in the city of Dijon, 1,146 strokes were identified in the SNDS and 1,188 in the registry. The proportion of strokes with anticoagulated AF was 13.4% in the SNDS and 20.3% in the Dijon Stroke Registry. Very similar characteristics were found between patients identified through the 2 databases. The overall prevalence of AF in stroke patients could be estimated only in the Dijon stroke registry and was 30.4% for the study period. Discussion/Conclusion: If administrative health databases can be a useful tool to study the epidemiology of anticoagulated AF in stroke patients, population-based stroke registries as the Dijon Stroke Registry remain essential to fully study the epidemiology of strokes with anticoagulated AF.

To compare healthcare use and the number of days of sickness benefits between people with anterior cruciate ligament (ACL) injury who received physiotherapy before and after ACL reconstruction (ACLR) and those who received physiotherapy after ACLR only. Secondary aim: to measure the association between the volume of preoperative healthcare and post-ACLR recovery. Each individual’s care pathway was extracted from a section of the French National Health Data System (SNDS) database (province: Pays de La Loire). The database was queried for the codes related to sickness benefits and healthcare utilization, including physiotherapy, medical and paramedical visits and procedures, medication, and medical equipment provided up to six months before and eighteen months after the ACLR. (Registry/number: ClinicalTrials.gov/NCT05737719). Based on the timing of physiotherapy, two subcohorts were created from the database: ‘prehabilitation’ (n = 513) for those receiving physiotherapy before and after ACLR; ‘no prehabilitation’ (n = 630) for those only receiving physiotherapy after ACLR. Before ACLR, healthcare use was higher for the ‘prehabilitation’ group, including the number of medical visits (3.9 ± 2.3 vs. 3.0 ± 1.9 univariate p < 0.001), analgesia (mild opioids 60.4% vs. 49.8% univariate p < 0.001), dispensing of medical equipment (85.0% vs. 68.9% univariate p < 0.001) and sickness benefit days (52.7 ± 45.6 days vs. 33.2 ± 35.8 days, univariate p < 0.001). After ACLR, the ‘prehabilitation’ group underwent a higher number of physiotherapy sessions (46.8 ± 21.9 sessions vs 35.8 ± 19.0 sessions, p < 0.001) but had a similar number of sickness benefit days (94.7 ± 77.8 days vs 87.1 ± 69.9 days, p = 0.092). From the multivariate analysis (n = 1143): age, comorbidities, the preoperative number of sickness benefit days, and the number of physiotherapy sessions before ACLR explained 24% of the variance in days of sickness benefits after ACLR. Prehabilitation was associated with higher healthcare utilization before and after ACLR. Prehabilitation, and other preoperative variables, explained only a part of the number of days of sickness benefits after ACLR.

Objectives: To investigate the clinical characteristics, epidemiology, survival estimates and healthcare resource utilization and associated costs in patients with systemic sclerosis-associated interstitial lung disease (SSc-ILD) in France.Methods: The French national administrative healthcare database, the Système National des Données de Santé (SNDS), includes data on 98.8% of the French population, including data relating to ambulatory care, hospitalizations and death. In our study, claims data from the SNDS were used to identify adult patients with SSc-ILD between 2010 and 2017. We collected data on clinical features, incidence, prevalence, survival estimates, healthcare resource use and costs.Results: In total, 3,333 patients with SSc-ILD were identified, 76% of whom were female. Patients had a mean age [standard deviation (SD)] of 60.6 (14.4) years and a mean (SD) individual study duration of 3.9 (2.7) years. In 2016, the estimated overall incidence and prevalence were 0.69/100,000 individuals and 5.70/100,000 individuals, respectively. The overall survival estimates of patients using Kaplan–Meier estimation were 93, 82, and 55% at 1, 3, and 8 years, respectively. During the study, 98.7% of patients had ≥1 hospitalization and 22.3% of patients were hospitalized in an intensive care unit. The total annual mean healthcare cost per patient with SSc-ILD was €25,753, of which €21,539 was related to hospitalizations.Conclusions: This large, real-world longitudinal study provides important insights into the epidemiology of SSc-ILD in France and shows that the disease is associated with high mortality, healthcare resource utilization and costs. SSc-ILD represents a high burden on both patients and healthcare services.Clinical Trial Registration:www.ClinicalTrials.gov, identifier: NCT03858842.

Snds Sas Export Import Data. Follow the Eximpedia platform for HS code, importer-exporter records, and customs shipment details.

Summary of process linkage steps between FREGAT and SNDS datasets.

Llc Snds Export Import Data. Follow the Eximpedia platform for HS code, importer-exporter records, and customs shipment details.

IntroductionPluriprofessional and coordinated healthcare use is recommended for Alzheimer's Disease and Related Diseases (ADRD). Despite a protective health system, France is characterized by persistent and significant social inequalities in health. Although social health inequalities are well documented, less is known about social disparities in healthcare use in ADRD, especially in France. Therefore, this study aimed to describe healthcare use according to socioeconomic deprivation among ADRD subjects and the possible potentiating role of deprivation by age.MethodsWe studied subjects identified with incident ADRD in 2017 in the French health insurance database (SNDS). We described a large extent of their healthcare use during the year following their ADRD identification. Deprivation was assessed through French deprivation index (Fdep), measured at the municipality level, and categorized into quintiles. We compared healthcare use according to the Fdep quintiles through chi-square tests. We stratified the description of certain healthcare uses by age groups (40–64 years, 65–74 years, 75–84 years, 85 years, and older), number of comorbidities (0, 1, 2–3, 4 comorbidities and more), or the presence of psychiatric comorbidity.ResultsIn total, 124,441 subjects were included. The most deprived subjects had less use of physiotherapy (28.56% vs. 38.24%), ambulatory specialists (27.24% vs. 34.07%), ambulatory speech therapy (6.35% vs. 16.64%), preventive consultations (62.34% vs. 69.65%), and were less institutionalized (28.09% vs. 31.33%) than the less deprived ones. Conversely, they were more exposed to antipsychotics (11.16% vs. 8.43%), benzodiazepines (24.34% vs. 19.07%), hospital emergency care (63.84% vs. 57.57%), and potentially avoidable hospitalizations (12.04% vs. 10.95%) than the less deprived ones.Discussion and conclusionThe healthcare use of subjects with ADRD in France differed according to the deprivation index, suggesting potential health renunciation as in other diseases. These social inequalities may be driven by financial barriers and lower education levels, which contribute to health literacy (especially for preventive care). Further studies may explore them.

The development of medico-administrative databases over the last few decades has led to an evolution and to a significant production of epidemiological studies on infectious diseases based on retrospective medical data and consumption of care. This new form of epidemiological research faces numerous methodological challenges, among which the assessment of the validity of targeting algorithm. We conducted a scoping review of studies that undertook an estimation of the completeness and validity of French medico-administrative databases for infectious disease epidemiological research. Nineteen validation studies and nine capture-recapture studies were identified. These studies covered 20 infectious diseases and were mostly based on the evaluation of hospital claimed data. The evaluation of their methodological qualities highlighted the difficulties associated with these types of research, particularly those linked to the assessment of their underlying hypotheses. We recall several recommendations relating to the problems addressed, which should contribute to the quality of future evaluation studies based on medico-administrative data and consequently to the quality of the epidemiological indicators produced from these information systems.

The Open LPP data offer consists of a set of annual databases, covering reimbursements and the number of beneficiaries of medical devices included in the list of products and services from 2014 to 2022. It provides additional information to the LppAM file. All data are extracted from the National Health Data System (SNDS). Data on the LPP are presented in an aggregated manner according to the Title and the first 2 sub-chapters (SC1, SC2), and in detail according to the LPP fine nomenclature (CODE_LPP). This offer is based on two types of datasets: Base complète sur les dépenses dispositifs médicaux The first Open_LPP datasets 2014 to 2022 make it possible to study the annual expenditure on medical devices (amounts reimbursed – REM – and reimbursable – BSE) as well as the annual quantity of medical devices (QTE), based on descriptive elements on the beneficiaries (age group, gender, region of residence according to the new INSEE nomenclature) or information on the prescriber’s specialty. Bases complémentaires enrichies des dénombrements de bénéficiaires The second datasets were compiled in addition to the Open_LPP databases 2014 to 2022 to report the number of beneficiaries of medical devices, at the aggregate level (Title and sub-chapters of level 1 and 2) as well as at the level of the detailed LPP nomenclature. These files are prefixed by NB and the year concerned. Then, the suffix indicates on the one hand the level of the nomenclature studied (TITLE: Title LPP; SC1: level 1 sub-chapter; SC2: level 2 sub-chapter; CODE_LPP: detailed nomenclature), on the other hand the additional breakdown criteria. For example, the NB_2014_TITRE_AGE_SEXE dataset plots the number of beneficiaries for each crossing of the different LPP TITLE with the age group and gender of the beneficiary in 2014. Each year, 32 bases are set up to meet the need and guarantee the non-double counting of beneficiaries.

Link to the ScienceBase Item Summary page for the item described by this metadata record. Service Protocol: Link to the ScienceBase Item Summary page for the item described by this metadata record. Application Profile: Web Browser. Link Function: information

This supplemental material provides additional methodological details, definitions, and results supporting the main analysis.

Data Source: Describes the French national health data system (SNDS), and approvals for study.

Marginal Structural Models (MSMs) with Inverse Probability Treatment Weighting (IPTW): Provides a detailed explanation of the statistical modeling framework used to estimate the causal effect of concomitant methotrexate (MTX) on TNFi persistence, including model specification, weight construction, stabilization, and truncation procedures.

Supplemental Tables I–VI:

Table I: Lists the ATC codes and definitions used to identify medication exposures.

Table II: Describes algorithms for identifying comorbidities and related diagnoses from administrative data.

Table III: Summarizes baseline characteristics of the study population across MTX exposure levels.

Table IV: Presents detailed distributions of continuous and categorical MTX exposure across dosing regimens and calculation methods over trimesters.

Table V: Reports results from sensitivity analyses assessing the robustness of the main findings under alternative definitions of MTX exposure.

Table VI: Provides subgroup analyses evaluating the impact of concomitant MTX use on adalimumab persistence.

Supplemental Figure 1: Displays a directed acyclic graph (DAG) illustrating the hypothesized relationships among biologic persistence, concomitant MTX use, and time-varying confounders.

Patient characteristics in FREGAT non-linked and FREGAT-SNDS linked populations.

Health Canada is offering an option for a rolling review of a new drug submission (NDS) or supplement to a new drug submission (SNDS) that meets specific eligibility conditions. Sponsors nearing the end of clinical development of a drug can apply to Health Canada for rolling review status for a forthcoming drug submission.

Tietokannan kuvaus:

• Tietokannan tavoitteet ja alkuperäiset tarkoitukset:

Synteettinen aineisto luotiin osana CNAM:n käyttämän algoritmin käännöstä ja toteutusta (link to the description sheet of the algorithm).

HDH:n mukauttamat Python- ja SAS-versiot kattavat synteettiset tiedot vuosilta 2018-2019, mutta niitä voidaan laajentaa muille vuosille. CNAM-lähdeohjelma kehitettiin SAS: ssä ja se toimii vuosina 2015-2019.

Edellä mainitun algoritmin tavoitteena on kohdistaa toimet diabeteksesta kärsiviin henkilöihin NSDS:n pääperustassa, jotta voidaan luoda CNAM:n luoman ja ylläpitämän patologiakartoituksen ”huippudiabetes” (versio G8).

• Luomisen tausta:

Huippudiabetesalgoritmin käyttöönotto edellytti synteettisten (kuvitteellisten) taulukoiden ja muuttujien mobilisointia.

-yhdistää vuositaulukot yhdeksi taulukoksi seuraavien osalta: ER_PRS_F, ER_ETE_F, ER_PHA_F,

Data/SNDS-yhteisö. - Tietokannan luomiseen liittyvät tulokset: CNAM: n käyttämä algoritmi ylimmän diabeteksen rakentamiseksi:(lähdeversio (CNAM), Python-versio ja SAS-versio (HDH)) (https://www.health-data-hub.fr/library-open-algorithms-health/algorithm-to-build-the-top-diabete-of-mapping).

Rekrytoi ihmisiä monilla eri aloilla työskentelemään Quebecissä, joka haluaa rekrytoida alueella.

• Keräysmenetelmä ja sisällyttämisperusteet:

Tietojen esitystapa:

Ohjelmissa käytetään HDH:n synteettisiä tietoja tietyin mukautuksin: Tämä tietoaineisto tuotettiin käyttäen NSDS:n vuoden 2019 päätietokantataulukoiden järjestelmää.

• Kohdeyleisö:

päivämäärämuodon muuntaminen muotoon yymmdd10.

Potilaan tunnistaminen perustuu tiettyjen lääkkeiden kohdentamiseen ja/tai ALD:hen ja/tai sairaalahoitoon leikkaussalissa.

NUM_ENQ:n nimeäminen uudelleen BEN_NIR_PSA:ksi, Kartoitusalgoritmien tavoitteena on maksimoida spesifisyys (ei herkkyys) eli varmistaa, että kohdepotilailla ei ole muita kuin diabeetikkoja.

• Muuttujien valinta:

Algoritmin käyttöönotto edellyttää seuraavien taulukoiden ja muuttujien mobilisointia (vaadittu historia on merkitty vastaavaan ruutuun):

Potilaita, joilla on alle kolme tiettyjen lääkkeiden annostelua, joilla ei ole ALD: tä ja jotka eivät ole olleet sairaalahoidossa viiden vuoden kuluessa diabeteksen vuoksi, ei säilytetä.

SAS:n ja Pythonin mukautetut ohjelmat perustuvat synteettiseen dataan vuosilta 2018 ja 2019. CNAM-lähdekoodi (SAS:ssa) suunniteltiin toimimaan vuosien 2015–2019 tietojen pohjalta.

Tämän tietokokonaisuuden rajat:

https://gitlab.com/healthdatahub/boas/cnam/top-diabete/-/raw/main/Tables_et_variables_du_SNDS_n%C3%A9cessaires.png?ref_type=p%C3%A4%C3%A4t" alt="syötä kuvan kuvaus tähän" title="syötä kuvan otsikko tähän"> lääketieteellisen johdonmukaisuuden puute, vuotuisten muutosten päivittämisen puute, kehittyvä taulukkojärjestelmä, joka voi olla epätäydellinen ja epätäydellinen.

Ohjelmaan ei sisälly analyysia sairausvakuutuksesta korvattavista arvioiduista menoeristä.

Algoritmi tunnistaa yleisimmät diabetespotilaat tiettynä vuonna (2019). Se ei määritä diabeteksen tarkkaa alkamispäivää pohjassa.

Vaikka synteettisten tietojen käyttö on hyödyllistä NSDS-tietojen manipuloinnissa, sillä on rajoituksia:

Lisätietoja tietokannan käytöstä huipputason diabetesohjelmien (CNAM) yhteydessä ohjelmien GitLab-tietokannassa (link of the GitLab repository).

Tuki:

Yhteyspiste: dir.donnees-SNDS@health-data-hub.fr

Rahoitusosuus:

Gitlabissa (tee lippu tai yhdistämispyyntö)

Sponsors will be issued an invoice for the full fee related to examining the NDS or SNDS once Health Canada has accepted the submission for review and issued the screening acceptance letter.